Category: Article

Feature Learning using Convolutional Denoising Autoencoder for Activity Recognition

The hand-crafted (time and frequency-based) features that are extracted and selected are heuristic and rely on expert knowledge of the domain. The features may be effective in certain specific settings, but the same features might fail to discriminate the activities in a more general environment. Furthermore, hand-crafted feature extraction and selection are time-consuming, laborious and […]

A Unified Generative Model using Generative Adversarial Network for Activity Recognition

One of the important factors of high accuracy of deep learning is the sufficient amount of training data. A robust and reliable model needs a vast amount of data to precisely capture the underlying pattern of the data. However, data collection in activity recognition is expensive and time-consuming especially from the elderly people. In this […]

We are still far from creating a true artificial intelligence

This is really a good read for anyone who is in the field of Artificial Intelligence (AI). Machine learning (an aspect/subset of AI), is the current trend or the buzzword in the technology world at the moment. It has been deployed in wide range of computing tasks e.g. self-driving car, automatic translation etc. It is […]

Activity Recognition using Convolutional Neural Network

Aging and dependent population is recognized as a major social and economic issues for the coming decades. According to World Health Organization, it is estimated that there will be 2 billion people of age 60 and older by 2050 [1]. Physical activity plays a major role in healthy ageing and one of the requirement to […]

Ontology-based sensor fusion for activity recognition

Context-aware activity recognition systems are dealing with heterogeneous sensors and these sensors are providing data at different sampling rate and output forms. Wearable sensors such as accelerometers and gyroscopes provide fast and real-time raw data which has to be interpreted before being useful to the application. Whereas ambient sensors such as temperature, humidity or object-interaction […]

Adaptive Sliding Window for Physical Activity Recognition

A sliding window with a fixed size is not an effective approach for activity recognition system. Misclassifications could still happen especially for transitional activities. This is due to the fact that the length of transitional activity signals varies depending on the time to complete the activity [1], [2]. To overcome the problem, the window size […]

Enhancing ontological reasoning with uncertainty handling for activity recognition

Handling uncertainty is a challenge in activity recognition. Uncertainty can be due to sensor errors (e.g. run out of batteries, imprecise outputs, missing activations etc.) and communication failures. and variability in human activities. These issues may significantly influence the accuracy of activity recognition. Data-driven approaches use machine learning techniques such as Decision Tree, naïve Bayes […]

Reasoning under uncertainty

Real-world is inherently uncertain. Uncertainty indicates the lack of confidence in an event or decision. It arises from different sources and in various forms. In the context of activity recognition, uncertainty may be due to sensor errors, communication failures and variability in human activities. Reasoning under uncertainty is a process of deducing new knowledge based […]

Comparison between evidence (Dempster-Shafer) theory and Bayesian theory

Both Bayesian theory and Dempster-Shafer (evidence) theory assign non-negative weights to set of events. In Bayesian theory, the finite set of possible events denoted by , each individual event, is assigned a non-negative weight called probability denoted by . The probabilities satisfy the following properties. for all In Dempster-Shafer theory, the finite set called frame […]